Transcribed Image Text: The value x 56 should be used in the model to predict the revenue of Amazon in what year? Amazon Revenue The annual revenue of Amazon is given in the table below (source). Amazon Annual Year Revenue (Billions of US dollars) 2020 386.064 2019 280.522 2018 232.887 2017 177.866 2016 135.987 2015 107.006 2014 88.988 2013 74.452 2012 61.093 2011 48.077 2010 34.204
Transcribed Image Text: The value x 56 should be used in the model to predict the revenue of Amazon in what year? Amazon Revenue The annual revenue of Amazon is given in the table below (source). Amazon Annual Year Revenue (Billions of US dollars) 2020 386.064 2019 280.522 2018 232.887 2017 177.866 2016 135.987 2015 107.006 2014 88.988 2013 74.452 2012 61.093 2011 48.077 2010 34.204
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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linear 3 this question isnt incomplete so please help me with that
Transcribed Image Text:**Question:**
The value \( x = 56 \) should be used in the model to predict the revenue of Amazon in what year?
**Transcribed Image Text:**
The value \( x = 56 \) should be used in the model to predict the revenue of Amazon in what year?
---
**Amazon Revenue**
The annual revenue of Amazon is given in the table below (source).
| Year | Amazon Annual Revenue (Billions of US dollars) |
|------|-----------------------------------------------|
| 2020 | 386.064 |
| 2019 | 280.522 |
| 2018 | 232.887 |
| 2017 | 177.866 |
| 2016 | 135.987 |
| 2015 | 107.006 |
| 2014 | 88.988 |
| 2013 | 74.452 |
| 2012 | 61.093 |
| 2011 | 48.077 |
| 2010 | 34.204 |
![**Title: Applying Linear Regression to Amazon's Revenue Data**
In this module, we will learn how to apply linear regression to a given data set and develop a predictive model. Specifically, we will use Amazon's annual revenue data to find a linear regression model that represents the relationship between time and revenue.
### Linear Regression Model
A linear regression model can be represented by the equation:
\[ y = mx + b \]
Where:
- \( y \) is Amazon's annual revenue in billions of US dollars,
- \( x \) is the number of years since 2010,
- \( m \) is the slope of the regression line,
- \( b \) is the y-intercept of the regression line.
### Task
Using the provided data table, apply linear regression to determine the values of \( m \) and \( b \) that best fit the data points. The result will be a linear equation that you can use to predict Amazon's revenue for any given year after 2010.
To proceed, ensure you understand the following steps in linear regression analysis:
1. **Plot the Data Points:** Create a scatter plot of the data points using the given \( x \) and \( y \) values.
2. **Calculate the Line of Best Fit:** Use the least squares method to calculate the slope (\( m \)) and y-intercept (\( b \)).
3. **Analyze the Results:** Interpret the slope and y-intercept in the context of Amazon's revenue growth over the years.
By following these steps, you will be able to predict future revenues based on the trend observed from past data, helping in financial forecasting and economic analysis.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fea6ddd78-5d51-4e2d-8762-122e3c25fe21%2F9acd7c7e-dbf0-4e3a-80e0-e4124253ce58%2Fswtqm6m_processed.png&w=3840&q=75)
Transcribed Image Text:**Title: Applying Linear Regression to Amazon's Revenue Data**
In this module, we will learn how to apply linear regression to a given data set and develop a predictive model. Specifically, we will use Amazon's annual revenue data to find a linear regression model that represents the relationship between time and revenue.
### Linear Regression Model
A linear regression model can be represented by the equation:
\[ y = mx + b \]
Where:
- \( y \) is Amazon's annual revenue in billions of US dollars,
- \( x \) is the number of years since 2010,
- \( m \) is the slope of the regression line,
- \( b \) is the y-intercept of the regression line.
### Task
Using the provided data table, apply linear regression to determine the values of \( m \) and \( b \) that best fit the data points. The result will be a linear equation that you can use to predict Amazon's revenue for any given year after 2010.
To proceed, ensure you understand the following steps in linear regression analysis:
1. **Plot the Data Points:** Create a scatter plot of the data points using the given \( x \) and \( y \) values.
2. **Calculate the Line of Best Fit:** Use the least squares method to calculate the slope (\( m \)) and y-intercept (\( b \)).
3. **Analyze the Results:** Interpret the slope and y-intercept in the context of Amazon's revenue growth over the years.
By following these steps, you will be able to predict future revenues based on the trend observed from past data, helping in financial forecasting and economic analysis.
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